Implementation details:
We follow the protocol in [1]} for all experiments. A batch size of 32 is used for all datasets. For training, images are resized to \(256\times 256\) , and then randomly cropped to \(224\times 224\)  and randomly flipped horizontally as in [1]}.
On CUB models are trained for 50 epochs, while on ImageNet and OpenImages models are trained for 10 epochs.
The learning rate is decayed by a factor of 0.1 after the 15th epoch for CUB and 3rd epoch for ImageNet and OpenImages.
